Enhancing acoustic comfort for earplug users: objective and subjective evaluation of bone-conducted sound with meta-earplugs incorporating Helmholtz resonators
Bibliographic record
Abstract
Passive earplugs are commonly used to protect workers from excessive noise exposure, but they often result in discomfort. The occlusion effect (OE) is a major discomfort that corresponds to an increased perception of bone-conducted sound at low frequencies. Objectively, the OE is associated with an increase in the sound pressure level generated in the earcanal under bone-conducted stimulation. Inspired by metamaterials, “ meta -earplugs” incorporating Helmholtz resonators have been developed to minimize this phenomenon, and their effectiveness has been validated using artificial ears in the authors’ prior work. In this study, 34 participants evaluated the effectiveness of meta -earplugs in reducing the OE. Three configurations of the meta -earplug were tested alongside a commercial foam earplug. Objective measurements of both OE and sound attenuation were conducted. Participants also completed a questionnaire evaluating their perception of low-frequency sound amplification and the judgement of the naturalness of their own voice while speaking with the earplugs. On average, the results demonstrate that meta -earplugs reduced the objective OE by up to 20 dB below 1 kHz. Additionally, the perception of low-frequency sound amplification decreased by 2 points, while voice naturalness judgement increased by 2 points, both assessed on a 7-point Likert scale. Using linear mixed-effects models, it was found that the perception of low-frequency sound amplification was primarily driven by the objective OE at 125 Hz, while voice naturalness was also significantly influenced by the objective OE at 4 kHz and the psychosocial characteristic of familiarity with the experimenter. Overall, meta -earplugs were preferred by 85 % of the participants.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".